A Self-Localization and Path Planning Technique for Mobile Robot - - PowerPoint PPT Presentation

A Self-Localization and Path Planning Technique for Mobile Robot Navigation

Jia-Heng Zhou and Huei-Yung Lin National Chung Cheng University 9th World Congress on Intelligent Control and Automation (WCICA), 2011

Presented by: Dereck Wonnacott Michigan Technological University Nov 26, 2012

Mobile Robotics - Core Problems

• 1. Localization

○ Given a map and sensor data, where is the robot? ○ Dead Reckoning ■ Encoders, Gyroscopes, Accelerometers, etc ○ External Sensing ■ LiDar, Cameras, GPS, Sonar ○ How can we integrate these data sources?

• 2. Path (Global) Planning

○ Given a map, start loc., & goal loc. find a safe path ○ large maps have a huge search space

• 3. Trajectory (Local) Planning

○ Given a path and current location, produce motor commands

Mobile Robot Platform

• Differential drive system
• On-board Netbook for computation
• Lidar Sensor

(Very simple!)

Lidar Basics

• Use a laser to measure distance to objects

○ Time of flight measurment ○ Sweep a wide area in fixed angular increments

• The resulting data is a set of points

○ Each point is an X,Y pair relative to the scanner where an object was detected

ICP Localization [2]

• ICP - Iterative Closest Point Algorithm

○ Requires a model of the environment (Map) ○ Receives laser sensor data (Data)

• Randomly place the Data around the Map

○ Empirically determine the required sample size

• Iteratively translate and rotate each sample

○ Apply a reasonably complex quartioneon based formula to each element of Data ○ Each iteration minimizes distance of each element in Data to the Map ○ Each sample will converge to a local minimum

• Select the best fitting sample

ICP Localization Results

Local ICP

• Once the robot is localized globally, save

computation by setting initial guess based on previous Localization results and wheel encoder data

• Translate the Data by your best guess on

robot motion since last ICP update

• Quickly converges and follows robot on Map

Path (Global) Planning

• 1. Corner Detection

○ Harris Corner Detection ○ (Graph Nodes)

• 2. Complete Graph

○ Every Possibility

• 3. Visibility Graph

○ Prune Search Space

• 4. Optimal Path

○ Global Plan

Harris Corner Detection [3]

(Very) Generally Speaking:

• 1. Compare each pixel to it's neighbors
• 2. Sum the squares of the differences
• 3. All local maxima represent a corner

2 1 5 2 3 2 3 3 2 2

Visibility Graph

For each edge in CompleteGraph If ( edge_has_no_obstructions ) VisibilityGraph += edge

Dijkstra Algorithm

• Famous, and efficient, graph search
• Yes, A* would probably work better

○ Dijkstra doesn't include distance heuristic

• The visibility graph reduces the search

space significantly, the search algorithm has little effect on performance in the author's examples.

Dijkstra_Animation (Wikipedia)

Global Planner Summary

Trajectory (Local) Planner

• A Repulsive force pushes the robot away

from obstacles

• An Attractive force pulls along the path

Local Planner

• p : Distance to Obstacle
• p0: Safety Distance
• Many obstacles will produce many forces
• Because of the visibility graph, the robot will

not get stuck in convex locations

Motivation for Repulsive forces

• Notice the final path

looks good in (c)

• It actually comes too

close to the corner of an object however

• The repulsive force

maintains reachability

Room for Improvement

• 1. 3D Lidar
• a. The robot would be able to sense out-of-plane
• bstacles
• 2. Localization based on sensed maps
• a. Each map currently needs to be made by hand
• b. Dozens of SLAM techniques exist for this
• 3. Inflate obstacles by the radius of the robot
• a. The eliminates paths where the robot cannot travel
• b. May also reduce the number of graph nodes
• c. Decreases the chance of needing the repulsive force

Take-Away

• Localization is Hard!

○ Luckily, there are many pre-existing algorithms

• Lidar works really really well

○ But it's expensive, $5000+

• The visibility graph is an awesome tool!

○ This it the paper's primary contribution to robotics

• Separate the Global and Local planners

○ Significant reduction in complexity in both

References

[1] Jia-Heng Zhou; Huei-Yung Lin; , "A self-localization and path planning technique for mobile robot navigation," Intelligent Control and Automation (WCICA), 2011 9th World Congress on , vol., no., pp.694-699, 21-25 June 2011 [2] Besl, P.J.; McKay, H.D.; , "A method for registration of 3-D shapes," Pattern Analysis and Machine Intelligence, IEEE Transactions on , vol.14, no.2, pp.239- 256, Feb 1992 [3] C. Harris and M. Stephens (1988). "A combined corner and edge detector". Proceedings of the 4th Alvey Vision Conference. pp. 147–151.